Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations586642
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory120.8 MiB
Average record size in memory216.0 B

Variable types

Numeric7
Text1
Unsupported1
DateTime1
Categorical17

Alerts

ACK Attack is highly overall correlated with TCP AnomalyHigh correlation
Avg packet len is highly overall correlated with SSDPHigh correlation
Data speed is highly overall correlated with IPv4 fragmentation and 1 other fieldsHigh correlation
High volume traffic is highly overall correlated with Suspicious trafficHigh correlation
IPv4 fragmentation is highly overall correlated with Data speed and 2 other fieldsHigh correlation
Packet speed is highly overall correlated with Data speed and 1 other fieldsHigh correlation
SSDP is highly overall correlated with Avg packet lenHigh correlation
Source IP count is highly overall correlated with IPv4 fragmentationHigh correlation
Suspicious traffic is highly overall correlated with High volume trafficHigh correlation
TCP Anomaly is highly overall correlated with ACK AttackHigh correlation
ACK Attack is highly imbalanced (> 99.9%) Imbalance
CHARGEN is highly imbalanced (99.8%) Imbalance
CLDAP is highly imbalanced (86.9%) Imbalance
CoAP is highly imbalanced (99.9%) Imbalance
DNS is highly imbalanced (90.1%) Imbalance
Generic UDP is highly imbalanced (90.4%) Imbalance
High volume traffic is highly imbalanced (52.1%) Imbalance
IPv4 fragmentation is highly imbalanced (99.3%) Imbalance
NTP is highly imbalanced (96.1%) Imbalance
RDP is highly imbalanced (99.9%) Imbalance
RPC is highly imbalanced (> 99.9%) Imbalance
SNMP is highly imbalanced (99.5%) Imbalance
SSDP is highly imbalanced (99.1%) Imbalance
SYN Attack is highly imbalanced (99.2%) Imbalance
Sentinel is highly imbalanced (> 99.9%) Imbalance
Suspicious traffic is highly imbalanced (57.9%) Imbalance
TCP Anomaly is highly imbalanced (> 99.9%) Imbalance
Source IP count is highly skewed (γ1 = 20.64839776) Skewed
Attack code is an unsupported type, check if it needs cleaning or further analysis Unsupported
Port number has 138720 (23.6%) zeros Zeros
Avg packet len has 95644 (16.3%) zeros Zeros

Reproduction

Analysis started2025-03-09 14:33:57.850240
Analysis finished2025-03-09 14:34:18.473736
Duration20.62 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

Attack ID
Real number (ℝ)

Distinct134769
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81774.73
Minimum1
Maximum134769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:18.533672image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3884
Q145107.25
median93556
Q3121627
95-th percentile131719
Maximum134769
Range134768
Interquartile range (IQR)76519.75

Descriptive statistics

Standard deviation42440.014
Coefficient of variation (CV)0.5189869
Kurtosis-1.105818
Mean81774.73
Median Absolute Deviation (MAD)32803
Skewness-0.49731565
Sum4.7972491 × 1010
Variance1.8011547 × 109
MonotonicityNot monotonic
2025-03-09T14:34:18.628096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95972 12534
 
2.1%
84340 6443
 
1.1%
24243 5300
 
0.9%
93249 4995
 
0.9%
126513 3573
 
0.6%
24113 3226
 
0.5%
84337 3155
 
0.5%
3188 2646
 
0.5%
51781 1966
 
0.3%
1043 1885
 
0.3%
Other values (134759) 540919
92.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 2
< 0.1%
4 1
< 0.1%
5 2
< 0.1%
6 1
< 0.1%
7 2
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
134769 1
 
< 0.1%
134768 2
< 0.1%
134767 1
 
< 0.1%
134766 1
 
< 0.1%
134765 2
< 0.1%
134764 3
< 0.1%
134763 1
 
< 0.1%
134762 2
< 0.1%
134761 4
< 0.1%
134760 2
< 0.1%

Detect count
Real number (ℝ)

Distinct12534
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.30195
Minimum1
Maximum12534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:18.714815image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median16
Q3176
95-th percentile1708
Maximum12534
Range12533
Interquartile range (IQR)174

Descriptive statistics

Standard deviation1199.8967
Coefficient of variation (CV)3.3395219
Kurtosis43.35277
Mean359.30195
Median Absolute Deviation (MAD)15
Skewness6.0803895
Sum2.1078162 × 108
Variance1439752.2
MonotonicityNot monotonic
2025-03-09T14:34:18.803064image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 134769
23.0%
2 48500
 
8.3%
3 24559
 
4.2%
4 16201
 
2.8%
5 12135
 
2.1%
6 9842
 
1.7%
7 8205
 
1.4%
8 7050
 
1.2%
9 6115
 
1.0%
10 5328
 
0.9%
Other values (12524) 313938
53.5%
ValueCountFrequency (%)
1 134769
23.0%
2 48500
 
8.3%
3 24559
 
4.2%
4 16201
 
2.8%
5 12135
 
2.1%
6 9842
 
1.7%
7 8205
 
1.4%
8 7050
 
1.2%
9 6115
 
1.0%
10 5328
 
0.9%
ValueCountFrequency (%)
12534 1
< 0.1%
12533 1
< 0.1%
12532 1
< 0.1%
12531 1
< 0.1%
12530 1
< 0.1%
12529 1
< 0.1%
12528 1
< 0.1%
12527 1
< 0.1%
12526 1
< 0.1%
12525 1
< 0.1%
Distinct18200
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:18.926803image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.1429833
Min length7

Characters and Unicode

Total characters4190374
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10068 ?
Unique (%)1.7%

Sample

1st rowIP_0001
2nd rowIP_0002
3rd rowIP_0003
4th rowIP_0003
5th rowIP_0002
ValueCountFrequency (%)
ip_0151 134155
22.9%
ip_0040 21553
 
3.7%
ip_0202 21322
 
3.6%
ip_0006 19880
 
3.4%
ip_0010 18257
 
3.1%
ip_0017 16867
 
2.9%
ip_0074 16759
 
2.9%
ip_0024 15194
 
2.6%
ip_0965 13592
 
2.3%
ip_15194 12608
 
2.1%
Other values (18190) 296455
50.5%
2025-03-09T14:34:19.121880image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4190374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4190374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4190374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 729485
17.4%
I 586642
14.0%
P 586642
14.0%
_ 586642
14.0%
1 516352
12.3%
5 294489
7.0%
2 178133
 
4.3%
4 167082
 
4.0%
6 137821
 
3.3%
9 116564
 
2.8%
Other values (3) 290522
 
6.9%

Port number
Real number (ℝ)

Zeros 

Distinct34137
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25066.609
Minimum0
Maximum65535
Zeros138720
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:19.212176image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153
median6881
Q351413
95-th percentile61994
Maximum65535
Range65535
Interquartile range (IQR)51360

Descriptive statistics

Standard deviation25775.209
Coefficient of variation (CV)1.0282687
Kurtosis-1.7678858
Mean25066.609
Median Absolute Deviation (MAD)6881
Skewness0.25110692
Sum1.4705126 × 1010
Variance6.6436138 × 108
MonotonicityNot monotonic
2025-03-09T14:34:19.295707image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138720
23.6%
4500 53741
 
9.2%
49261 53365
 
9.1%
443 43330
 
7.4%
51413 22287
 
3.8%
80 19350
 
3.3%
60645 18397
 
3.1%
53 16013
 
2.7%
22000 7024
 
1.2%
63396 4925
 
0.8%
Other values (34127) 209490
35.7%
ValueCountFrequency (%)
0 138720
23.6%
1 1
 
< 0.1%
12 36
 
< 0.1%
20 24
 
< 0.1%
22 2876
 
0.5%
23 20
 
< 0.1%
25 1146
 
0.2%
53 16013
 
2.7%
68 1
 
< 0.1%
80 19350
 
3.3%
ValueCountFrequency (%)
65535 19
< 0.1%
65534 2
 
< 0.1%
65533 4
 
< 0.1%
65532 3
 
< 0.1%
65531 7
 
< 0.1%
65529 4
 
< 0.1%
65528 5
 
< 0.1%
65527 1
 
< 0.1%
65526 3
 
< 0.1%
65525 13
< 0.1%

Attack code
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size4.5 MiB

Packet speed
Real number (ℝ)

High correlation 

Distinct6173
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78997.206
Minimum10500
Maximum3906400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:19.381193image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10500
5-th percentile51300
Q156800
median64500
Q377300
95-th percentile140100
Maximum3906400
Range3895900
Interquartile range (IQR)20500

Descriptive statistics

Standard deviation84232.366
Coefficient of variation (CV)1.0662702
Kurtosis425.22554
Mean78997.206
Median Absolute Deviation (MAD)9100
Skewness17.076545
Sum4.6343079 × 1010
Variance7.0950915 × 109
MonotonicityNot monotonic
2025-03-09T14:34:19.474046image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51600 2731
 
0.5%
51000 2720
 
0.5%
50400 2651
 
0.5%
51300 2651
 
0.5%
51900 2629
 
0.4%
54000 2619
 
0.4%
52200 2606
 
0.4%
56400 2563
 
0.4%
52800 2528
 
0.4%
52500 2489
 
0.4%
Other values (6163) 560455
95.5%
ValueCountFrequency (%)
10500 1
< 0.1%
10600 1
< 0.1%
11800 1
< 0.1%
12600 1
< 0.1%
12800 1
< 0.1%
13100 1
< 0.1%
13900 1
< 0.1%
14000 1
< 0.1%
14100 1
< 0.1%
14300 2
< 0.1%
ValueCountFrequency (%)
3906400 1
< 0.1%
3905500 1
< 0.1%
3882900 1
< 0.1%
3850000 1
< 0.1%
3763300 1
< 0.1%
3761200 1
< 0.1%
3731900 1
< 0.1%
3684100 1
< 0.1%
3677400 1
< 0.1%
3314900 1
< 0.1%

Data speed
Real number (ℝ)

High correlation 

Distinct1523
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.254136
Minimum0
Maximum2744
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:19.560086image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q168
median80
Q396
95-th percentile146
Maximum2744
Range2744
Interquartile range (IQR)28

Descriptive statistics

Standard deviation83.816683
Coefficient of variation (CV)0.96060412
Kurtosis270.61114
Mean87.254136
Median Absolute Deviation (MAD)14
Skewness13.279122
Sum51186941
Variance7025.2363
MonotonicityNot monotonic
2025-03-09T14:34:19.639053image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 15894
 
2.7%
73 15192
 
2.6%
74 14327
 
2.4%
75 13506
 
2.3%
76 13196
 
2.2%
77 12790
 
2.2%
78 12456
 
2.1%
71 12032
 
2.1%
5 12002
 
2.0%
79 11922
 
2.0%
Other values (1513) 453325
77.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 102
 
< 0.1%
3 5113
0.9%
4 8296
1.4%
5 12002
2.0%
6 10238
1.7%
7 4738
 
0.8%
8 1274
 
0.2%
9 731
 
0.1%
ValueCountFrequency (%)
2744 1
< 0.1%
2711 1
< 0.1%
2704 1
< 0.1%
2691 1
< 0.1%
2685 1
< 0.1%
2678 1
< 0.1%
2657 1
< 0.1%
2644 1
< 0.1%
2641 2
< 0.1%
2635 1
< 0.1%

Avg packet len
Real number (ℝ)

High correlation  Zeros 

Distinct1447
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean974.81376
Minimum0
Maximum1518
Zeros95644
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:19.739284image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1594
median1263
Q31439
95-th percentile1498
Maximum1518
Range1518
Interquartile range (IQR)845

Descriptive statistics

Standard deviation567.81661
Coefficient of variation (CV)0.58248727
Kurtosis-0.93527468
Mean974.81376
Median Absolute Deviation (MAD)213
Skewness-0.8574472
Sum5.718667 × 108
Variance322415.7
MonotonicityNot monotonic
2025-03-09T14:34:19.829260image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95644
 
16.3%
66 14588
 
2.5%
1064 14206
 
2.4%
780 13955
 
2.4%
1028 13406
 
2.3%
1506 11509
 
2.0%
1296 9260
 
1.6%
1475 8245
 
1.4%
1486 7545
 
1.3%
1498 6793
 
1.2%
Other values (1437) 391491
66.7%
ValueCountFrequency (%)
0 95644
16.3%
21 1
 
< 0.1%
47 2
 
< 0.1%
50 1
 
< 0.1%
51 14
 
< 0.1%
52 12
 
< 0.1%
57 1
 
< 0.1%
63 2
 
< 0.1%
65 34
 
< 0.1%
66 14588
 
2.5%
ValueCountFrequency (%)
1518 5379
0.9%
1517 126
 
< 0.1%
1516 12
 
< 0.1%
1515 20
 
< 0.1%
1514 21
 
< 0.1%
1513 22
 
< 0.1%
1512 282
 
< 0.1%
1511 11
 
< 0.1%
1510 244
 
< 0.1%
1509 28
 
< 0.1%

Source IP count
Real number (ℝ)

High correlation  Skewed 

Distinct2658
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.902049
Minimum0
Maximum11557
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.5 MiB
2025-03-09T14:34:19.919763image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q36
95-th percentile158
Maximum11557
Range11557
Interquartile range (IQR)5

Descriptive statistics

Standard deviation258.85221
Coefficient of variation (CV)7.0145754
Kurtosis561.67489
Mean36.902049
Median Absolute Deviation (MAD)1
Skewness20.648398
Sum21648292
Variance67004.466
MonotonicityNot monotonic
2025-03-09T14:34:20.015983image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 265496
45.3%
2 58781
 
10.0%
3 45778
 
7.8%
4 36755
 
6.3%
5 29168
 
5.0%
6 22546
 
3.8%
7 17169
 
2.9%
8 13140
 
2.2%
9 10123
 
1.7%
10 8222
 
1.4%
Other values (2648) 79464
 
13.5%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 265496
45.3%
2 58781
 
10.0%
3 45778
 
7.8%
4 36755
 
6.3%
5 29168
 
5.0%
6 22546
 
3.8%
7 17169
 
2.9%
8 13140
 
2.2%
9 10123
 
1.7%
ValueCountFrequency (%)
11557 1
< 0.1%
11458 1
< 0.1%
10769 1
< 0.1%
10696 1
< 0.1%
10569 1
< 0.1%
10561 1
< 0.1%
10263 1
< 0.1%
10227 1
< 0.1%
10214 1
< 0.1%
10150 1
< 0.1%

Time
Date

Distinct528045
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
Minimum2022-08-08 18:09:36
Maximum2023-04-27 12:36:44
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T14:34:20.109549image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:20.197853image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ACK Attack
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586638 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586638
> 99.9%
1 4
 
< 0.1%

Length

2025-03-09T14:34:20.277604image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:20.336634image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586638
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586638
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586638
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586638
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586638
> 99.9%
1 4
 
< 0.1%

CHARGEN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586582 
1
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586582
> 99.9%
1 60
 
< 0.1%

Length

2025-03-09T14:34:20.400030image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:20.460062image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586582
> 99.9%
1 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586582
> 99.9%
1 60
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586582
> 99.9%
1 60
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586582
> 99.9%
1 60
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586582
> 99.9%
1 60
 
< 0.1%

CLDAP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
575984 
1
 
10658

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 575984
98.2%
1 10658
 
1.8%

Length

2025-03-09T14:34:20.523356image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:20.582686image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 575984
98.2%
1 10658
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 575984
98.2%
1 10658
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 575984
98.2%
1 10658
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 575984
98.2%
1 10658
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 575984
98.2%
1 10658
 
1.8%

CoAP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586624 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586624
> 99.9%
1 18
 
< 0.1%

Length

2025-03-09T14:34:20.644949image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:20.703449image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586624
> 99.9%
1 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586624
> 99.9%
1 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586624
> 99.9%
1 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586624
> 99.9%
1 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586624
> 99.9%
1 18
 
< 0.1%

DNS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
579120 
1
 
7522

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579120
98.7%
1 7522
 
1.3%

Length

2025-03-09T14:34:20.766384image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:20.826157image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579120
98.7%
1 7522
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 579120
98.7%
1 7522
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579120
98.7%
1 7522
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579120
98.7%
1 7522
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579120
98.7%
1 7522
 
1.3%

Generic UDP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
579402 
1
 
7240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 579402
98.8%
1 7240
 
1.2%

Length

2025-03-09T14:34:20.888869image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:20.949152image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 579402
98.8%
1 7240
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 579402
98.8%
1 7240
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 579402
98.8%
1 7240
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 579402
98.8%
1 7240
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 579402
98.8%
1 7240
 
1.2%

High volume traffic
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
1
526205 
0
60437 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 526205
89.7%
0 60437
 
10.3%

Length

2025-03-09T14:34:21.011395image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.071266image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 526205
89.7%
0 60437
 
10.3%

Most occurring characters

ValueCountFrequency (%)
1 526205
89.7%
0 60437
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 526205
89.7%
0 60437
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 526205
89.7%
0 60437
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 526205
89.7%
0 60437
 
10.3%

IPv4 fragmentation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586295 
1
 
347

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586295
99.9%
1 347
 
0.1%

Length

2025-03-09T14:34:21.139161image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.205181image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586295
99.9%
1 347
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 586295
99.9%
1 347
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586295
99.9%
1 347
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586295
99.9%
1 347
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586295
99.9%
1 347
 
0.1%

NTP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
584161 
1
 
2481

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 584161
99.6%
1 2481
 
0.4%

Length

2025-03-09T14:34:21.270105image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.328718image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 584161
99.6%
1 2481
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 584161
99.6%
1 2481
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 584161
99.6%
1 2481
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 584161
99.6%
1 2481
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 584161
99.6%
1 2481
 
0.4%

RDP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586610 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586610
> 99.9%
1 32
 
< 0.1%

Length

2025-03-09T14:34:21.392449image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.451445image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586610
> 99.9%
1 32
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586610
> 99.9%
1 32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586610
> 99.9%
1 32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586610
> 99.9%
1 32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586610
> 99.9%
1 32
 
< 0.1%

RPC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586641 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586641
> 99.9%
1 1
 
< 0.1%

Length

2025-03-09T14:34:21.515495image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.573872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586641
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586641
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586641
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586641
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586641
> 99.9%
1 1
 
< 0.1%

SNMP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586408 
1
 
234

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586408
> 99.9%
1 234
 
< 0.1%

Length

2025-03-09T14:34:21.637353image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.695853image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586408
> 99.9%
1 234
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586408
> 99.9%
1 234
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586408
> 99.9%
1 234
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586408
> 99.9%
1 234
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586408
> 99.9%
1 234
 
< 0.1%

SSDP
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586197 
1
 
445

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586197
99.9%
1 445
 
0.1%

Length

2025-03-09T14:34:21.760102image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.821384image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586197
99.9%
1 445
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 586197
99.9%
1 445
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586197
99.9%
1 445
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586197
99.9%
1 445
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586197
99.9%
1 445
 
0.1%

SYN Attack
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586230 
1
 
412

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586230
99.9%
1 412
 
0.1%

Length

2025-03-09T14:34:21.884628image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:21.947079image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586230
99.9%
1 412
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 586230
99.9%
1 412
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586230
99.9%
1 412
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586230
99.9%
1 412
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586230
99.9%
1 412
 
0.1%

Sentinel
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586637 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586637
> 99.9%
1 5
 
< 0.1%

Length

2025-03-09T14:34:22.011436image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:22.070585image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586637
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586637
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586637
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586637
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586637
> 99.9%
1 5
 
< 0.1%

Suspicious traffic
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
536583 
1
 
50059

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 536583
91.5%
1 50059
 
8.5%

Length

2025-03-09T14:34:22.131121image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:22.192746image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 536583
91.5%
1 50059
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 536583
91.5%
1 50059
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 536583
91.5%
1 50059
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 536583
91.5%
1 50059
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 536583
91.5%
1 50059
 
8.5%

TCP Anomaly
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
586630 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters586642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 586630
> 99.9%
1 12
 
< 0.1%

Length

2025-03-09T14:34:22.255053image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:34:22.312064image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 586630
> 99.9%
1 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 586630
> 99.9%
1 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 586630
> 99.9%
1 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 586630
> 99.9%
1 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 586642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 586630
> 99.9%
1 12
 
< 0.1%

Interactions

2025-03-09T14:34:15.869666image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.384419image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.961574image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.541034image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.139501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.724612image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.289595image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.951543image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.468844image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.042940image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.626267image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.225456image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.808784image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.373409image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:16.030488image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.551744image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.121033image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.710124image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.308509image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.888347image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.457323image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:16.109291image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.631917image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.204006image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.795015image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.390181image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.969121image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.539510image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:16.195003image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.719076image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.288717image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.885338image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.477088image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.051686image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.627789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:16.271124image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.796695image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.375790image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.964711image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.556863image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.129016image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.705996image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:16.351248image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:12.881025image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:13.458458image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.057816image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:14.641309image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.209135image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:34:15.788239image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-03-09T14:34:22.373054image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ACK AttackAttack IDAvg packet lenCHARGENCLDAPCoAPDNSData speedDetect countGeneric UDPHigh volume trafficIPv4 fragmentationNTPPacket speedPort numberRDPRPCSNMPSSDPSYN AttackSentinelSource IP countSuspicious trafficTCP Anomaly
ACK Attack1.0000.0090.0030.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.505
Attack ID0.0091.0000.3120.0170.2450.0080.255-0.0800.2800.2160.4480.0470.083-0.205-0.2360.0310.0010.0610.0480.0370.0050.2930.4750.016
Avg packet len0.0030.3121.0000.0250.3120.0200.1860.1950.1060.1990.3060.0420.457-0.190-0.0450.0560.0090.0590.6980.0480.0280.0550.2670.007
CHARGEN0.0000.0170.0251.0000.0000.0000.0720.1930.0000.0000.0300.1560.1430.1750.0090.0000.0000.0000.0000.0000.0000.1430.0330.000
CLDAP0.0000.2450.3120.0001.0000.0000.0570.3780.2460.0150.2030.0030.0090.3400.1300.0000.0000.0020.0030.0030.0000.0060.1980.000
CoAP0.0000.0080.0200.0000.0001.0000.0000.0000.0000.0000.0080.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.000
DNS0.0000.2550.1860.0720.0570.0001.0000.3820.0230.0130.2580.1930.0100.3620.1060.0080.0000.1470.0030.0020.0000.4640.2520.000
Data speed0.000-0.0800.1950.1930.3780.0000.3821.000-0.1830.0140.1990.8120.0880.6370.1380.0000.0000.1840.0000.0000.000-0.1370.2180.000
Detect count0.0000.2800.1060.0000.2460.0000.023-0.1831.0000.0280.0880.0050.016-0.046-0.1950.0000.0000.0030.0060.0060.0000.2950.0990.000
Generic UDP0.0000.2160.1990.0000.0150.0000.0130.0140.0281.0000.3300.0020.0070.0100.1070.0000.0060.0010.0020.0020.0000.0070.0340.000
High volume traffic0.0070.4480.3060.0300.2030.0080.2580.1990.0880.3301.0000.0410.1780.2390.1770.0210.0010.0400.0780.0780.0080.1570.9010.013
IPv4 fragmentation0.0000.0470.0420.1560.0030.0000.1930.8120.0050.0020.0411.0000.0210.7090.0340.0000.0000.0120.0000.0000.0000.6600.0430.000
NTP0.0000.0830.4570.1430.0090.0000.0100.0880.0160.0070.1780.0211.0000.2590.0630.1120.0000.0000.0160.0000.0000.0940.0820.000
Packet speed0.000-0.205-0.1900.1750.3400.0000.3620.637-0.0460.0100.2390.7090.2591.0000.2130.0880.0170.2910.0520.0000.0000.0520.2470.000
Port number0.006-0.236-0.0450.0090.1300.0020.1060.138-0.1950.1070.1770.0340.0630.2131.0000.0510.0000.0190.0340.0260.000-0.0720.2220.009
RDP0.0000.0310.0560.0000.0000.0000.0080.0000.0000.0000.0210.0000.1120.0880.0511.0000.0000.0000.0000.0000.0000.2100.0010.000
RPC0.0000.0010.0090.0000.0000.0000.0000.0000.0000.0060.0010.0000.0000.0170.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
SNMP0.0000.0610.0590.0000.0020.0000.1470.1840.0030.0010.0400.0120.0000.2910.0190.0000.0001.0000.0000.0000.0000.3140.0060.000
SSDP0.0000.0480.6980.0000.0030.0000.0030.0000.0060.0020.0780.0000.0160.0520.0340.0000.0000.0001.0000.0000.0000.0000.0620.000
SYN Attack0.0000.0370.0480.0000.0030.0000.0020.0000.0060.0020.0780.0000.0000.0000.0260.0000.0000.0000.0001.0000.0000.0000.0080.000
Sentinel0.0000.0050.0280.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
Source IP count0.0000.2930.0550.1430.0060.0000.464-0.1370.2950.0070.1570.6600.0940.052-0.0720.2100.0000.3140.0000.0000.0001.0000.1500.000
Suspicious traffic0.0000.4750.2670.0330.1980.0000.2520.2180.0990.0340.9010.0430.0820.2470.2220.0010.0000.0060.0620.0080.0000.1501.0000.000
TCP Anomaly0.5050.0160.0070.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2025-03-09T14:34:16.485415image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-09T14:34:17.290655image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Attack IDDetect countVictim IPPort numberAttack codePacket speedData speedAvg packet lenSource IP countTimeACK AttackCHARGENCLDAPCoAPDNSGeneric UDPHigh volume trafficIPv4 fragmentationNTPRDPRPCSNMPSSDPSYN AttackSentinelSuspicious trafficTCP Anomaly
011IP_00014500[High volume traffic]5560073138362022-08-08 18:09:3600000010000000000
121IP_00024500[High volume traffic]6350090150612022-08-08 18:37:2800000010000000000
231IP_00031200[High volume traffic]5970079139912022-08-08 18:41:2500000010000000000
332IP_00031200[High volume traffic]6570086139912022-08-08 18:41:2600000010000000000
441IP_00024500[High volume traffic]5950085148612022-08-08 18:47:4900000010000000000
551IP_000412347[High volume traffic]74800108151812022-08-08 18:57:1500000010000000000
652IP_000412347[High volume traffic]81700118151812022-08-08 18:58:1100000010000000000
761IP_000545574[Suspicious traffic]891002124612022-08-08 19:09:2900000000000000010
871IP_00014500[High volume traffic]6110081140432022-08-08 19:11:3600000010000000000
972IP_00014500[High volume traffic]75400100138632022-08-08 19:11:3700000010000000000
Attack IDDetect countVictim IPPort numberAttack codePacket speedData speedAvg packet lenSource IP countTimeACK AttackCHARGENCLDAPCoAPDNSGeneric UDPHigh volume trafficIPv4 fragmentationNTPRDPRPCSNMPSSDPSYN AttackSentinelSuspicious trafficTCP Anomaly
5866321347641IP_001060513[High volume traffic]6000075132042023-04-27 12:31:0500000010000000000
5866331347642IP_00100[High volume traffic]5340067132252023-04-27 12:31:0600000010000000000
5866341347651IP_002348529[High volume traffic]7310085118522023-04-27 12:31:1000000010000000000
5866351347661IP_178270[High volume traffic]5220074150612023-04-27 12:31:2000000010000000000
5866361347652IP_002361167[High volume traffic]6000067125752023-04-27 12:32:2800000010000000000
5866371347671IP_174660[High volume traffic]5130064132512023-04-27 12:32:2900000010000000000
5866381347681IP_01574631[High volume traffic]6900083126612023-04-27 12:32:5100000010000000000
5866391347682IP_01574631[High volume traffic]5420065126812023-04-27 12:32:5200000010000000000
5866401347643IP_001052288[High volume traffic]6970088131872023-04-27 12:35:1800000010000000000
5866411347691IP_004060339[High volume traffic]6050073127832023-04-27 12:36:4400000010000000000